Information Processing and Wireless Energy Harvesting
in Interference-Aware Public Safety Networks
Daniyal Munir1•Syed Tariq Shah1•Kae Won Choi1• Min Young Chung1
Published online: 11 June 2018
Ó Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract In a public safety environment, user equipments (UEs) located within the cov-erage area of evolved NodeB, relay network services to out-of-covcov-erage UEs. However, relay UEs in public safety environments are typically energy constrained and cannot operate indefinitely without recharging. Radio frequency energy harvesting has been proposed as a solution for recharging wireless UEs. In this paper, we propose a scheme for extending the lifetime of a public safety network by wirelessly charging relay UEs. In addition, we propose a relay selection method considering the battery status of relay UEs. The proposed relay selection is defined as a bipartite graph matching problem and the optimal relay is obtained through matching games technique. The proposed scheme not only improves the network lifetime but also extend the network coverage. We also conduct system level simulations to evaluate the performance of the proposed scheme. Simulation results show that the overall performance of the system is improved in terms of achievable throughput and network lifetime.
Keywords Energy harvesting Public safety Device-to-device communications Network lifetime
& Min Young Chung [email protected] Daniyal Munir [email protected] Syed Tariq Shah [email protected] Kae Won Choi [email protected]
1
College of Information and Communication Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
1 Introduction
In public safety situations, reliable communication services should be supported where network infrastructure is partially damaged [1]. Third Generation Partnership Project (3GPP) has been making efforts for evolving a broadband public safety network based on Long Term Evolution (LTE). Device-to-device (D2D) communications enable LTE net-works to become one of the potential solutions to provide reliable network services in public safety environments [2]. Figure1 shows an example of such network, where in-coverage user equipments (UEs) relay information between evolved NodeB (eNB) and out-of-coverage isolated UEs (IUEs) [3].
Employing D2D communications for public safety services poses many challenges, such as, device discovery, resource allocation, relay UE (RUE) selection, battery power of these RUEs, and so on [2, 4]. Among them two important problems require primary attention for improving performance of relay networks. One problem is the limited battery life of RUEs. Since network life time of D2D-based public safety network highly depends on the battery power of RUEs, an efficient battery recharge mechanism may significantly improve the overall network performance. The other problem is the selection of RUEs to provide reliable network services to IUEs. An efficient relay selection algorithm can improve the performance of IUEs and extend network coverage [5].
Energy harvesting from radio frequency (RF) signals is an attractive solution to recharge wireless UEs [6]. In this technique, the RF radiation is captured by the receiving antennas and converted into direct current (DC) voltage through rectifier circuits [7]. As RF signals carry information and energy at the same time, simultaneous information processing and wireless energy harvesting has been studied recently [8–11].
Energy harvesting techniques can be used to prolong the operating time of UEs without replacing their batteries [12]. Especially, it can be useful for recharging the batteries of UEs in public safety environments, where infrastructure has been damaged. Huge amount of researches have been done in this field; see [13] and the references therein. However, its design for public safety environments has not been investigated. Wireless energy har-vesting should be given appropriate consideration for extending the battery life of RUEs in mission critical public safety situations.
On the other hand, D2D communications-based coverage extension has been exten-sively studied for both legacy cellular networks and public safety environments [14–19].
However, these works have not considered any interference for RUE selection process, unlike real situations. Interference severely effects the desired signal, if multiple RUEs transmit information signals using the same channel, simultaneously. Therefore, for the selection of RUEs, interference should be taken into account. Preliminary version of this work has been presented in a conference, which briefly discusses the selection of energy harvesting RUEs in an interference free environment [20].
In this paper, we propose an RF energy harvesting-based battery recharging mechanism for RUEs. To the best of our knowledge, energy harvesting technique for D2D-based relay network in public safety scenario has not been studied in previous works. Main objective of our proposed scheme is to prolong the network lifetime of a D2D-based public safety network. For this purpose, we investigate the use of energy harvesting RUEs, which are also used to extend the network coverage in a public safety environment. This provides a more accurate characterization of system performance than our previous work [19], where RUEs rely solely on their battery power. Furthermore, to exploit the inherent diversity gain of relaying, we also propose an RUE selection scheme. The proposed selection scheme considers the battery status of candidate RUE in addition to the eNB-RUE link capacity. We conduct system level simulations to study the effect of different parameters on the throughput of the proposed D2D-based public safety network.
The rest of the paper is organized as follows. The next section discusses the related works. Section3introduces a system model and energy harvesting techniques. A proposed relay selection scheme is presented in Sect.4. Performance evaluation is provided in Sect.5and the paper is concluded in Sect.6.
2 Related Work
The concept of relaying information using legacy cellular UEs has been addressed in previous works. In [15], the authors have used RUEs to extend the coverage of femtocells. In this RUE assisted heterogeneous cellular network, idle UEs opportunistically offload from macrocell to femtocell. Nishiyama et al. have proposed a network architecture based only on smart phones, which can relay messages to multi-hop distance without using network infrastructure [16]. In [21], authors have proposed a network topology composed of UEs as virtual infrastructure nodes, providing both capacity gains and enhanced network coverage. However, these schemes consider an interference-free environment, which is unrealistic in a wireless communication setup.
The benefits of an efficient relay selection scheme are many folds, such as, increased throughput and energy efficiency, reduced signaling overhead, less number of relay re-selection and so on. Three RUE re-selection schemes are proposed in [22], namely, best relay selection, relay cooperation and relay ordering. These schemes are based on channel state information (CSI) feedback to maximize the end-to-end transmission rate given a certain outage probability. Another relay selection scheme is proposed in [19] for public safety situations, where the radial velocity of RUEs is also considered along with CSI feedback. However, these schemes may fail to fully exploit the benefits if the selected RUEs do not have sufficient battery power to support IUEs. Therefore, sustainable battery power supply should be available for RUEs in public safety situations.
Recently, energy harvesting from RF signals has become a sustainable solution to recharge the power constrained devices [8]. An excessive amount of researches have been carried out in this field. Initially, the research in wireless energy harvesting and information
processing has considered one-hop communication systems and studied rate-energy (R-E) tradeoff [9,10]. For realization of simultaneous wireless information and power transfer (SWIPT), a practical receiver architecture has been proposed in [11], where the perfor-mance of two receiver strategies, time switching (TS) and power splitting (PS) has been analyzed.
RF energy harvesting has also been investigated for wireless relay networks. Nasir et al. in [23] have proposed two relaying protocols based on PS and TS schemes, TS-based relaying (TSR) and PS-based relaying (PSR). The relay switches in time between energy harvesting and information decoding for TSR protocol. On the other hand, in PSR, the received power at relay is split in two portions one for energy harvesting and the other for information decoding. The power constraint relay harvests energy from received RF sig-nals first and then forwards the source sigsig-nals to the destination, utilizing the harvested energy. Authors extend this work to two-way relay network for TSR scheme in [24]. In [25], the authors further evaluate the throughput of two-way relay network for TSR, PSR and a hybrid of TSR and PSR (HPTSR) schemes.
Furthermore, Huang et al. in [26] have provided key features of a wireless powered cellular network (WPCN). A discussion about practical implementation of SWIPT in cellular networks is also given. Several design issues such as range of transferring power, safety in WPCN, interference with wireless communications and powering UEs through energy harvesting are also addressed in this work. Fundamental challenges such as designing receiver architecture for SWIPT from base station in the downlink, SNR outage regions in the uplink due to doubly near-far problem, broadband energy harvesting and mutltiuser scheduling in WPCNs have been pointed out in [27]. To tackle these issues, several guidelines such as deployment of dedicated energy source, characterization of SNR zones, coordination among eNBs and on request energy transfer protocols are also suggested.
The performance of energy harvesting-based D2D communications in a cognitive network has been recently investigated for different spectrum access policies [28]. The authors have shown that energy harvesting can be a reliable alternative to power cognitive D2D transmitters while achieving acceptable performance. D2D communications under-lying cellular networks, where D2D transmitter harvests energy from ambient sources has been studied in [29]. To maximize the sum-rate of D2D links, authors have proposed a joint resource block and power allocation scheme. A framework of the D2D communi-cations is developed for energy harvesting-based heterogeneous cellular networks by accounting for the energy harvesting parameters, eNB density, and the UE density [30]. Authors in [31] have proposed a wireless energy transfer scheme, where eNB and nearby UEs transfer energy to a battery starved UE. The network operator is responsible for managing energy transfer between these entities. These works provide the feasibility of using energy harvesting in D2D-based cellular networks. However, energy harvesting-based D2D communication has not been studied for a public safety environment where network infrastructure is partially damaged.
All these works are very affective for the recent advancements in SWIPT for cooper-ative networks. Our aim is to investigate D2D-based relay communications with energy harvesting capabilities in public safety networks. In this article, we focus on the selection of energy constraint RUEs based on eNB-RUE link capacity and battery status of RUEs. We investigate its gains in terms of network lifetime and range extension. We first find an optimal RUE for a single IUE and then globally optimize the relay selection process so that the selected RUE cast minimum interference to other UEs.
3 System Model
A three-tier cellular network consisting of 19 hexagonal cells has been considered, where eNB is equipped with 3-sectored antennas deployed in the center of each cell. For PS situation, we consider that there are only k active cells and all the UEs in other cells are located out of the coverage area of these active cells, called IUEs. We assume that the network employs frequency division duplex (FDD) with separate uplink (UL) and downlink (DL) channels and a dedicated spectrum for IUEs termed as side-link (SL) channel [32]. We consider a Rayleigh block-fading channel, where the channel gain is invariant over each scheduling time and may vary independently from one scheduling time to another.
UEs are randomly and uniformly deployed in each cell, where UEs that directly communicate with the eNB are called cellular UEs (CUEs). For a cellular link to be established with the eNB, a minimum downlink wireless access network (WAN) signal-to-interference-plus-noise-ratio (SINR) is required. IUEs are unable to communicate directly with eNB because they do not achieve minimum required WAN SINR from the eNB of the active cells. Hence, IUEs require an intermediate node to assist their transmission to the eNB, called RUEs.
Each scheduling time block T is divided into two time slots, i.e. in the first slot T/2 eNB transmits the signals to RUE and in the next T/2 slot RUE forwards the received signal to IUE. There are M number of IUEs and N number of cellular UEs that are acting as candidate RUEs. We denote IUE set as I and candidate RUE set as J , where I ¼ f1; 2; 3; . . .; Mg and J ¼ f1; 2; 3; . . .; Ng. The SINR between IUEs and RUEs is known as D2D-SINR. We assume that all the UEs in the network move with a random speed and direction. We use random way point (RWP) mobility model to represent the movement of the UEs. To be more specific, the UE pauses for fixed time at a location and the change it’s destination, speed and direction randomly and independently of other UEs. Also, RUEs are equipped with single receiver antennas having SWIPT capabilities.
The set of eNBs is denoted asK ¼ f1; 2; . . .; Og, where each eNB is a multiuser system which serves multiple UEs simultaneously. The channel between eNB k and UE j expe-riences independent Rayleigh fading with complex channel fading gain hk;j. It is assumed that channel state information of all the in-coverage UEs is known to the eNB, which schedules them on orthogonal UL and DL channels to avoid interference. However, cell-edge UEs may experience co-channel interference from neighboring eNBs as shown in Fig.2a. The received signal at the UE j is then given as
yk;j¼ ffiffiffiffiffi Pk p jhk;jjxk ffiffiffiffiffiffiffi dm k;j p þ X k0:k06¼k ffiffiffiffiffiffi Pk0 p jhk0;jjx k0 ffiffiffiffiffiffiffiffi dm k0;j q þ nj; ð1Þ
where Pkis the received power from eNB k; xkis the received information signal, dk;jis the distance between eNB k and UE j, m is the pathloss exponent and njis the additive white Gaussian noise (AWGN) with zero mean and variance r2j at UE j.
For energy harvesting, we adopt a power splitting receiver at UE j that splits the power of the received signals into two portions, q portion of the power for energy harvesting and 1 q for information processing as shown in Fig.2b. UE j harvests energy from the received information signals and the interference signals for a duration of T/2 and hence the harvested energy is obtained as
Ek;j¼ gq Pkjhk;jj2 dm k;j þ X k0:k06¼k Pk0jh k0;jj 2 dm k0;j 0 @ 1 AT=2; ð2Þ
where g is energy conversion efficiency with values varying from 0 to 1 depending upon the energy harvesting circuitry. After q portion of the received signal power is used for energy harvesting, remaining portion of the received signal can be written as
y0k;j¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1 qÞPk dm k;j s jhk;jjxkþ X k0:k06¼k ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1 qÞPk0 dm k0;j s jhk0;jjxk0þ nj: ð3Þ
The IUEs which reside out of the coverage area of active eNBs, receive the signals from the intermediate node between IUE and eNB. The channel coefficient from a potential RUE j to IUE i can be represented as hj;i. The power consumed by the transmit/receive circuitry at the relay is assumed negligible as compared to the power utilized for trans-mitting signals [33]. Therefore, we suppose that all the harvested energy from received signals of eNB is used by the RUE j for recharging its battery.
The transmitted signal by RUE j is received at IUE i and can be given by
yj;i¼ ffiffiffiffiffiffi Pj dm j;i s jhj;ijxjþ X j0:j06¼j ffiffiffiffiffiffiffi Pj0 dm j0;i s jhj0;ijxj0 þ ni; ð4Þ where Pjis the transmission power of RUE j,Pj0:j06¼jPj0 is the sum of transmission power of all the interfering RUEs, hj;iis the channel fading gain from RUE j to IUE i; xjis the transmitted information signal, dj;i is the distance between UE j and IUE i, m is the pathloss exponent and niis the AWGN at IUE i, which is assumed to have zero mean and variance r2i.
Similarly, when IUE i wants to send its data to eNB it sends the information signal to its selected RUE. The received signal at RUE j from IUE i is given as
(a) (b)
yi;j¼ ffiffiffiffiffiffi Pi dm i;j s jhi;jjxiþ X i0:i06¼i ffiffiffiffiffiffiffi Pi0 dm i0;j s jhi0 ;jjxi0 þ nj; ð5Þ
where Piis the transmission power of IUE i; hi;j is the channel fading gain from IUE i to RUE j and xiis the transmitted information signal. RUE j harvests energy from the power of the received information signal of IUE i and the interfering signals of other IUEs and it can be given as Ei;j¼ gq Pijhi;jj2 dm i;j þ X i0:i06¼i Pi0jh i0;jj 2 dm i0;j 0 @ 1 AT=2: ð6Þ
After energy is harvested from the q portion of the information signal and interference signals, the remaining 1 q portion is sent for information decoding. The signal received for information processing is written as
y0i;j¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1 qÞPi dm i;j s jhi;jjxiþ X i0:i06¼i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1 qÞPi0 dm i0;j s jhi0;jjxi0þ nj: ð7Þ By Shanon’s formula rðxÞ ¼ b log2ð1 þ xÞ, throughput of each link over their respective channels can be calculated.
4 Proposed Scheme
4.1 Battery RechargingFor the case where RUEs rely only on the harvested energy to transmit data, the accu-mulated energy should be greater than the consumed energy i.e., Eq;j Ec;8 q 2 fK [ I g. Ec denotes the energy consumed by RUE j to carry out each transmission. Since energy harvested in a single time block is relatively small as compared to the consumed energy, RUE j accumulates energy for U number of time blocks for one transmission. To calculate the number of time blocks needed to accumulate this energy is complex because of the random nature of channel coefficient and distance between RUE j and eNB k/IUE i. However, average number of time blocks needed to accumulate enough energy for one transmission can be obtained from the following condition,
X U1 x¼1 EðxÞq;j\Ec XU x¼1 EðxÞq;j; 8 q 2 fK [ Ig and U[ 2 ð8Þ
where Eq;jðxÞdenotes the amount of harvested energy in time block x.
3GPP has specified a fixed transmission power for RUEs in public safety [34], and harvested energy in one time block is not enough to carry out transmission. So, the harvested energy will be stored in the rechargeable battery of RUEs for later use. In our proposed scheme, RUEs do not rely solely on the harvested energy for carrying out relay operations. RUEs use battery power to relay information to IUEs. The harvested energy, in each time block, is added to the batteries of RUEs and serves as an additional source of power for RUEs. From the viewpoint of long network time, the small amount of harvested energy in each time block can be used to extend the operating time of RUEs.
The aggregate energy harvested at RUE j is the sum of harvested energy from the received signals of both eNB and IUEs and can be written as
Ejtot¼ Ek;jþ Ei;j: ð9Þ
The harvested energy is stored in a battery with a storage size of Bmax. The battery size of all the RUEs is assumed to be identical with a discrete battery model. Specifically, the energy level of each RUE battery is quantized into L identical intervals. The current energy level of the RUE battery is denoted by Bj and can be given by
Bj¼ B0þ Eq;j Ec; 8 q 2 fK [ Ig ð10Þ
where B0is the previous energy level of the battery. With a discrete battery model each RUE has to report the quantized value of the energy level in the battery. This may reduce the number of feedback bits todlogL
2e, where de denotes the ceiling function [35]. Intu-itively, more feedback bits will be used for a large L, resulting in a reduced quantization error. The proposed energy harvesting scheme for RUEs in public safety is summarized in Algorithm 1.
Algorithm 1 Wireless energy harvesting in public safety UE relaying
1: I = {i|i = 1, 2, · · · , M} Set of IUEs
2: J = {j|j = 1, 2, · · · , N} Set of candidate RUEs 3: K = {k|k = 1, 2, · · · , O} Set of active eNBs
4: procedure UE relaying procedure using energy harvesting RUEs 5: eNBk transmits radio signals in its coverage area.
6: if yk,j received at RUEj from eNB k then 7: Harvestρ portion of signal power as (2) 8: end if
9: RUEj broadcasts its availability to requesting IUE i 10: if yi,j received at RUEj from IUE i then
11: Harvestρ portion of signal power as (6) 12: end if
13: for (j = 1; j <= N ; j + +) do
14: Accumulate total harvested energy atj as (10) 15: end for
16: end procedure
4.2 Relay Selection
In this section, we propose the relaying procedure for public safety environments, where CUEs act as relays. A CUE receives reference signals from the eNB and discovery signals from IUEs present in its proximity. CUE obtains the CSI from these received signals and calculates the achievable throughput for each link. Based on the achievable throughput, CUE broadcasts its status of whether or not it will take the role of RUE. Each RUE-assisted transmission from eNB to IUE and vice versa, is divided into two phases. In the first phase, eNB/IUE transmits signals to RUE and in the next phase, RUE forwards the signals to IUE/ eNB. As RUE uses PS receiver, it harvests the energy from a portion of received signal power in each phase and calculates the throughput from the remaining portion of the received signal power.
Throughput is considered as the main performance metric for a relay-assisted system such as D2D-based network in public safety environments. This is the reason we take into
account the capacity of each link for the selection of RUEs. In the first phase, eNB k periodically broadcasts reference signals in its coverage area. Candidate RUE j located in the coverage area of eNB k, receives the reference signals and estimate the amount of energy it can harvest from the received signal power according to (2). Because of the power splitting receiver structure used at RUE j, the remaining signal power is used by j for information processing. Note that RUE j may also receive reference signals from other active eNBs, which may cause interference to j. The received signal-to-interference-plus-noise ratio (SINR) at RUE, is given by
ck;j¼ ð1 qÞPkjhk;jj2 r2 rþ P k0:k06¼kð1 qÞPk0jhk0;jj 2: ð11Þ
This SINR is used to calculate the link capacity by using Shanon capacity formula as
rk;j¼ b log2ð1 þ ck;jÞ: ð12Þ
IUE i understands that it is located out of the coverage area of eNB if it does not receive any reference signals from its attached eNB. In the same phase, if IUE i has some data to transmit, it initiates discovery procedure by broadcasting service request message in its proximity. All the candidate RUEs in the proximity of IUE i respond to this services request message. This response message also contains the eNB-RUE link capacity and it’s battery state information. Based on this information, IUE i selects a more reliable RUE for its communication with the eNB. From received response message of each RUE, IUE i calculates the SINR of this link as
cj;i¼ Pjjhj;ij 2 r2 dþ P j0:j06¼jPj0jhj0;ij 2: ð13Þ
Similar approach is used to find the capacity of this link
rj;i¼ b log2ð1 þ cj;iÞ: ð14Þ
To this point, IUE i has all the necessary information to select a reliable RUE. In other words, a subset (Ji) of candidate RUEs is defined from the view point of IUE i. These RUEs are not only able to decode the information from eNB but also have enough energy to forward the data to IUEs. For each candidate RUE, IUE i has the CSI of both the links, i.e. eNB-RUE and RUE-IUE. IUE i is now able to select a reliable RUE with the highest CSI as j¼ max j2Ji minfck;j;cj;ig subject to : Bj;i Bth; ð15Þ where Bj;i¼ max8j2JiBj is the maximum measured value of battery level of candidate RUEs inJi.
This corresponds to the max-min fairness metric, which is an efficient metric for selecting DF relays [36]. It is an appropriate solution for selecting DF relays and its implementation complexity is low. This RUE selection metric represents the preferences of the IUE i and disregards the selection preferences of other IUEs. However, when there is a conflict between the preferences of two IUEs, the selection should be globally optimized. After identifying the most appropriate RUE for a single IUE, now the goal of the RUE selection metric is to optimize the following summation
max f1;2;...;Ng
X j
minfck;j;cj;ig: ð16Þ
Given the sets of IUEs and RUEs, this problem is precisely a bipartite graph matching problem, where the value of metric is considered as the weight on the edge between an IUE and a candidate RUE. At this point, the aim of the RUE selection scheme is to select the RUE for each IUE in such a way that is best for overall system performance. We map our RUE selection algorithm as a weighted bipartite matching problem [37]. As defined before the set of IUE (with N elements) and set of RUEs (with M elements) were represented byI andJ , respectively. Each vertex in the graph is represented by an element in I and J and vertex of each set has an edge with the vertex of the other. The weight of the edge between i2 I and j 2 J is
wi;j¼ minfck;j;cj;ig ð17Þ
Finally, the bipartite graph is denoted as G¼ ðI [ J ; I J Þ [37], whereI [ J repre-sents all vertices andI J represents all edges. Weight matrix is denoted by W with size M N, which has the elements wi;j. For example, there are three candidate RUEs for four IUEs to select, and assume the weight matrix as
W¼ 4 4 3 0 1 2 4 1 0 3 4 3 0 @ 1 A ð18Þ
This particular system is depicted in Fig.3, which shows a bipartite graph. There is a weight line between each IUE and RUE. To integrate the distributed design of RUE selection, the optimal stable state (OSS: stable state with maximal utility) is defined as this terminology is used in matching games [38].
The distributed implementation of the proposed selection metric is explained here, which is established on the approach of stable state. Different steps involved in this distributed selection are explained in the following and are summarized in Algorithm 2. First, each IUE i and RUE j maintains a preference list PLiand PLj, respectively. Taking an IUE i for example, the PListores all the candidate RUEs in the descending order according to their weights, i.e., more priority is given to the most suitable RUE for IUE i. Then, each IUE i has its own list matched(i) specifying its selected RUE. Each RUE j has a list matched(j) consisting of the IUE which can be chosen. Each RUE j also maintains a list candidates(j), which consists of those IUEs that have requested for the selection of RUE j. When all the IUEs are tagged as ‘‘matched’’, the algorithm ends, i.e., RUEs have been assigned to all the IUEs.
Algorithm 2 Proposed relay selection algorithm
1: Preference listP LiforI = {i|i = 1, 2, · · · , M} 2: Preference listP LjforJ = {j|j = 1, 2, · · · , N} 3: procedure Weighted bipartite matching problem 4: Initialize
5: matched(i) = ∅, ∀i ∈ I
6: matched(j) = ∅, candidates(j)= ∅, ∀j ∈ J 7: while ∃i ∈ I subject to: matched(i) = ∅ do 8: for i ∈ I do
9: if matched(i) = ∅ then
10: j∗← most suitable RUE in P Li 11: candidates(j∗)← candidates(j∗) {i} 12: Removej∗fromP Li
13: end if 14: end for 15: for j ∈ J do
16: candidates(j) ← candidates(j) matched(j)
17: i∗← first ranked element in candidates(j) according to P Lj
18: i∗∗← matched(j)
19: matched(i∗∗) =∅
20: matched(i∗) =j and matched(j)= i∗ 21: candidates(j) = ∅
22: end for 23: end while 24: end procedure
25: matched(i) is the final selection, ∀i ∈ I
When the algorithm is triggered for the first time (lines 8–14), each IUE, without being matched, sends request to the most suitable RUE from PLi. The candidate list of the corresponding RUE stores this request (line 11). Those IUEs which are accepted from corresponding RUEs are ‘‘matched’’ and will not request again. If IUE’s request is denied, Fig. 3 Network scenario depicted as a Bipartite graph
the requested RUE is eliminated from PLi, to avoid requesting the same RUE for next instance (line 12).
In the next part of the algorithm (lines 15–22), each RUE choose its favorite IUE from already labeled as ‘‘matched’’ and those that requested (stored in the list candidates(j)) this time. The selected IUE will be the one which is labeled ‘‘matched’’ this time. If the selected IUE is different from already matched IUE, the original IUE is reset to ‘‘not matched’’ (lines 18–20). As the algorithm ends, no RUE will deviate to a more favorable RUE, and IUE can not select a better RUE without outperforming the selection criteria of another IUE, which shows that the system will be stable. An IUE will select a new RUE if the battery power of the selected RUE is drained and it can no longer assist the commu-nication between IUE and eNB.
The complexity of this algorithm is O(MN). In the beginning, N IUEs request to select their most favorite RUEs. Then, any RUE may search for more suitable IUE, which is possible for maximum N 1 times even if the RUE attempts all the available IUEs. In addition, since M RUEs are present in the system, the worst case will be Nþ MðN 1Þ repetition of the algorithm, which makes the complexity of the MN order.
The proposed scheme is summarized as follows. IUEs should determine the weight wi;j for each candidate RUE. At first, the channel estimation from eNB k to candidate RUE j is calculated. When a candidate RUE j broadcasts its status along with the information about ck;j in its proximity, IUEs receive this information and can also estimate ci;j from the received signals. Now IUEs can calculate the weight wi;j, and store it in its preference list. IUEs then send the weight to candidate RUE, which upon receiving also stores these weights in its own preference list. For this purpose, MN transmissions of a real number are required. When the Algorithm 2 starts, the request from IUE for selecting its RUE can be transmitted through 1-bit information, which will be responded with another 1-bit infor-mation by the RUE, to announce the approval or denial of the request.
5 Performance Evaluation
In this section, we evaluate the performance of our proposed scheme. The performance evaluation is carried out using event-driven simulation developed in C language [40]. These simulations are executed by using 3GPP recommended UE relaying procedure.
Table 1 Simulation parameters
Parameters Studied value
Number of cells 19
Inter-cell distance 1732 m
Pathloss exponent (m) 2
Carrier frequency 2.4 GHz and 700 MHz
Bandwidth 10 MHZ for UL, DL and SL
Number of UEs per cell 30–300
Mobility model RWP (speed: 0–5 m/s)
Tx power eNB to UE 46 dBm
Tx power UE to eNB 24 dBm
Tx power UE to UE 31 dBm
Noise power density - 174 dBm/Hz
Different events, such as, packet generation, WAN scheduling, WAN transmission, D2D scheduling, D2D transmission and UE relay re-selection, are defined for step-by-step implementation of the simulations. The parameters of interests include network life time and expected throughput of both uplink and downlink channels from eNB to IUE via RUE. We have considered the data transmission throughput as the main performance metric for this relaying network as it represents the quality of service of public safety applications in a better way. The results show how these parameters are affected by the varying number of UEs present in the network. Also, the impact of energy harvesting ratio q on these parameters are described.
The considered network for simulation environment consists of 19 three-sectored cells, where only 3 cells are active and other cells are switched off to emulate a public safety environment, as recommended by 3GPP [32]. The inter-site distance between each cell is 1732 m. We assume that the network employs frequency division duplexing with 10 MHz for each UL and DL channel at the center frequency of 2.4 GHz and a 10 MHz channel for SL at 700 MHz. Studied values of other important parameters are summarized in Table1 and are in accordance with the 3GPP’s specification.
There are 30–300 UEs deployed randomly and uniformly in each cell, with a step size of 30. The mobility of all the UEs is illustrated by random waypoint mobility model, with a speed uniformly distributed between 0 and 5 m/s and no pauses. The UEs present in the active cells communicate with the associated eNB through direct UL and DL channels. These UEs can also serve as RUEs for IUEs present in the coverage area of non-active cells. RUEs and IUEs communicate with each other on direct D2D links. RUEs present in a cell experience co-channel interference from other active cells while communicating with eNB on the DL channel and from other IUEs on SL channel while communicating with its attached IUEs. Full buffer traffic model is considered for all the UEs.
RUEs harvest energy from both the information and interference signals received on DL and SL channels. The received signal power is split into two portions according to q and 1 q. To provide additional battery power to RUEs, energy is harvested from the q signal power. The remaining (1 q) signal power is used for further information processing.
( = 0.7) ( = 0.3)
Note that at least -6 dBm signal power is required for successful information processing [39].
At first, we investigate the achievable throughput of the network for 30 UEs, deployed in each cell. The initial battery power of each UE is set to 3000 mAh, which is a common specification for most of the smart phones commercially available. Battery of a UE is consumed when a UE is active, transmits and receives signals. Harvested energy from the received RF signals is converted into electrical power, which is added to the battery of RUEs for recharging. Throughput of each link between IUE and eNB is shown for varying values of q. In addition, a comparison is shown when there is no energy harvesting at RUEs.
Achievable throughput at the RUE from eNB is affected by the power splitting factor (q). This is because for higher values of q, more power is used for energy harvesting, which results in lower throughput as compared to the lower values of q. Figure4shows the achievable throughput of this link with respect to time. The monotonically decreasing relationship between achievable throughput and time is due to decreasing number of active links between eNB and RUEs. In other words, after a certain period of time, the batteries of UEs drain and the number of RUEs decreases, which leads to lower throughput.
The achievable throughput at eNB is not directly effected by q, because eNB has it’s own power and does not perform energy harvesting. However, the UL traffic generated by RUEs may vary according to energy harvesting q. The energy harvesting RUEs prolong their battery life, however, the throughput decreases because less power is available for information decoding for higher values of q. RUEs relay the same decoded information towards eNB which is lower for higher values of q. Figure5 shows the results for the achievable throughput at eNB from RUEs and CUEs.
Furthermore, the achievable throughput at RUEs from its attached IUEs is shown in Fig.6. Energy harvesting increases the battery life of RUEs which enables them to communicate for longer period of time. The impact of energy harvesting is shown in this figure and it can be observed that for lower values of q the achievable throughput is higher. The RUE harvests energy from all the received signals either from its attached IUEs or
( = 0.7) ( = 0.3)
from other interfering IUEs. The interfering IUEs degrade the performance of RUEs in terms of throughput.
The link between IUE and RUE is of significant importance and its performance directly effects the performance of end-to-end link between IUE and eNB. Figure7 shows the achievable throughput at IUE from RUEs. As there is no energy harvesting at IUEs, there is no direct impact of q on the achievable throughput of this link. However, the throughput decreases for higher values of q, because less information will be forwarded by RUEs.
( = 0.7) ( = 0.3)
Fig. 6 Achievable throughput at RUE from IUE (D2D UL)
( = 0.7) ( = 0.3)
(a) (b)
(c) (d)
Fig. 8 Different states of network at t¼ 0 s and t ¼ 2000 s with varying value of q. a State of network for q¼ 0:0; 0:30; 0:7at t = 0s. b State of network for q ¼ 0:0at t = 2000S. c State of network for q ¼ 0:30at t = 2000s. d State of network for q¼ 0:7at t = 2000s
( = 0.7) ( = 0.3)
To show the effect of proposed scheme on the battery life of UEs, we capture the state of network at two time stamps i.e. t¼ 0 and t ¼ 2000. For varying values of q, Fig.8 shows different sates of UEs present in the network when number of UEs per cell is set to 30. There are only three active eNBs and the corresponding cells are highlighted in the figure. In Fig.8, attached RUEs are those UEs that are acting as relays for IUEs and those RUEs which have zero battery are named as drained RUEs. Furthermore, IUEs that are attached with RUEs are called attached IUEs and those IUEs that cannot find a suit-able RUE are unattached IUEs. Finally, the UEs communicating with eNB directly on UL and DL channels are called as CUEs.
At t¼ 0, the state of all the UEs is same for each value of q, which is shown in Fig.8a. Note that there are no drained RUEs at t¼ 0 because we consider that the battery of each UE is fully charged. For q¼ 0, which means without energy harvesting, the number of active UEs is small at t¼ 2000. At this stage of time, most of the UEs drain their battery power which is shown in Fig.8b. The number of active UEs increases as the value of q increases for the same time t¼ 2000. This is because of the fact that proposed scheme uses RF energy harvesting for recharging the batteries of RUEs as shown in Fig.8c, d.
We observe the total throughput of the network while deploying different number of UEs in each cell. The total throughput of the network is calculated until all the RUEs are alive and active. Figure9shows the total DL throughput with varying number of UEs. It can be observed that as the number of UEs in each cell increases the total throughput of the network increases initially. However, when the number of UEs per cell reaches a certain point (240 in Fig.9) congestion occurs due to increased interference and the total network throughput starts decreasing.
Similarly, Fig.10shows total UL throughput with different number of UEs deployed in each cell. We observe same trends as shown in Fig.9 but the overall throughput of UL channel is less than that of DL channel. In addition to the low transmit power of UEs as compared to eNB, increasing number of IUEs will cause severe interference to RUEs, which results in decreased throughput.
( = 0.7) ( = 0.3)
The accumulate harvested power by RUEs in the proposed scheme is shown in Fig.11 with respect to time for different values of q. It can be observed from the figure that the accumulate harvested power linearly increase in time. Also, the rate of increase for higher values of q is greater as compared to the lower values of q. This is because of the fact that a larger portion of signal power is utilized for energy harvesting, in case of higher values of q. After a certain period in time, the number of RUEs is decreased and there will be less available RUEs for energy harvesting.
6 Conclusion
In this article, we have proposed a UE relaying procedure, where relay UEs harvest energy from the source signals and interference signals from the sources of other relay UEs. The source can be eNB while RUE relay information to the IUE and it can be IUE when RUE relay its information to the eNB. UE relaying helps in extending the coverage area of eNB, specifically in the public safety environment, where the network access is partially available. In addition to this, the energy harvesting at the relay UEs helps extend its battery life, hence the network coverage and battery life time is improved for a better overall performance. The performance metric used for the evaluation of the proposed scheme is achievable throughput and battery life time of available RUEs. The performance is eval-uated using system level simulation and the results are shown for varying number of UEs present in a cell.
Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (2014R1A5A1011478).
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Daniyal Munirreceived his B.S. degree in electrical (telecommuni-cation) engineering from COMSATS Institute of Information Tech-nology Lahore, Pakistan, in 2010. He is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering at Sung-kyunkwan University. His research interests include public safety networks, LTE-Advanced networks, wireless energy harvesting and device-to-device communication.
Syed Tariq Shah received his B.S. degree in telecommunication engineering from Baluchistan University of Information Technology, Engineering and Management Sciences, Pakistan, in 2009. He recently received his Ph.D. degree from the Department of Electrical and Computer Engineering at Sungkyunkwan University. He is also
affil-iated with Dept. of Telecommunication Engineering, FICT,
Balochistan University of Information Technology, Engineering and Management Sciences, Pakistan. His research interests include 5G networks, LTE-Advanced networks, wireless energy harvesting and device-to-device communication.
Kae Won Choi received the B.S. degree in Civil, Urban, and
Geosystem Engineering in 2001, and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science in 2003 and 2007, respectively, all from Seoul National University, Seoul, Korea. From 2008 to 2009, he was with Telecommunication Business of Samsung Electronics Co., Ltd., Korea. From 2009 to 2010, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. From 2010 to 2016, he was an assistant professor in the Department of Computer Science and Engineering, Seoul National University of Science and Technol-ogy, Korea. In 2016, he joined the faculty at Sungkyunkwan Univer-sity, Korea, where he is currently an associate professor in the School of Electronic and Electrical Engineering. His research interests include RF energy transfer, visible light communication, device-to-device communication, cognitive radio, radio resource management. He has served as an editor of IEEE Communications Surveys and Tutorials from 2014 and an editor of IEEE Wireless Communications Letters from 2015.
Min Young Chung received the B.S., M.S., and Ph.D. degrees in
electrical engineering from the Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 1990, 1993, and 1999, respec-tively. From January 1999 to February 2002, he was a Senior Member of Technical Staff with the Electronics and Telecommunications Research Institute, where he was engaged in research on the devel-opment of multiprotocol label switching systems. In March 2002, he joined the Faculty of Sungkyunkwan University, Suwon, Korea, where he is currently a Professor with the College of Information and Communication Engineering. His research interests include D2D Communications, Software-Defined Networking (SDN), 5G wireless communication networks, and wireless energy harvesting. He worked as an editor on the Journal of Communications and Networks from January 2005 to February 2011, and is a member of IEEE, IEICE, KICS, KIPS and KISS.
